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One Size Does Not Always Fit All in Value Assessment
Anirban Basu, PhD; Richard Grieve, PhD; Daryl Pritchard, PhD; and Warren Stevens, PhD
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One Size Does Not Always Fit All in Value Assessment

Anirban Basu, PhD; Richard Grieve, PhD; Daryl Pritchard, PhD; and Warren Stevens, PhD
Laying a clear path for incorporating reliable evidence on heterogeneity in value assessments could improve their applicability for healthcare decision making.
Am J Manag Care. 2019;25(11):540-542
Rising healthcare costs have led to the emergence of a host of value frameworks aimed at both defining and quantifying what value means for healthcare in the United States.1 Healthcare organizations, patient advocacy groups, and think tanks across the country have developed such frameworks to assess the potential value of new therapies.2,3 In the United States, the framework to address new drug evaluation and pricing developed by the Institute for Clinical and Economic Review (ICER) has caught the attention of private payers.4 Most recently, CVS Caremark announced plans to use the results from ICER’s cost-effectiveness assessments to guide formulary decision making, which could lead to the exclusion of some high-cost drugs from some of its plans.5

The ICER perspective on what value means in healthcare—and some of the core methodology that it uses to evaluate alternative technologies—is based on long-standing academic concepts about cost-effectiveness analyses. These have been used in decision making outside the United States, notably by the likes of the National Institute for Health and Care Excellence in England and Wales and the Pharmaceutical Benefits Advisory Committee in Australia. However, these approaches have faced criticism,6 not least because of the lack of attention given to heterogeneity in relative effectiveness and cost-effectiveness according to patients’ characteristics and preferences.7

The Second Panel on Cost-Effectiveness in Health and Medicine called for heterogeneity to be considered through the presentation of subgroup-specific cost-effectiveness, where appropriate evidence exists.8 Yet comparative and cost-effectiveness analyses have been slow to recognize heterogeneity and tend not to present subgroup value estimates.9 By focusing on evaluating the overall average effectiveness, these value frameworks do not encourage the generation of useful evidence on heterogeneity that can inform differential decisions about the extent to which particular subgroups may benefit from new, high-cost healthcare technologies.

In most published value assessments, globally, heterogeneity has not been featured strongly in the reports of the main clinical results, and in the cost-effectiveness analysis these are addressed post hoc, after the main model has been built. For example, ICER’s Evidence Rating Matrix10 makes no mention of whether a study attempts to detect or understand heterogeneity or report results by subgroup. There are genuine reasons to ignore heterogeneity in the absence of evidence, while there are cases where heterogeneity is ignored even with reliable evidence. ICER reports highlight both such cases.

One example in which evidence on heterogeneity could have been incorporated was ICER’s report on treatments for rheumatoid arthritis (RA). ICER stated, “RA remains a remarkably complex disease to diagnose and manage. There are multiple phenotypic and genotypic variations in the pathogenesis of the disease that affect both the course of RA and the outcome of therapy.”11 Still, no attempt was made to evaluate cost-effectiveness of different therapeutic agents for subgroups. It is important to note that there was no direct evidence about treatment-effect heterogeneity across subgroups in any of the trials that were identified for the report. However, evidence beyond those trials clearly suggested that for patients receiving the control regimen, clinical responses differed according to age and functional status.12 Hence, even if the relative effect of a new targeted immune modulator was constant across subgroups, there could still be substantial variation in the absolute effect scale required for estimates of cost-effectiveness.13 It is not clear how incorporating such heterogeneity might have changed the overall assessment, but at the least, it could have triggered a different conversation around value for certain groups of patients. More generally, ignoring heterogeneity could result in therapies that may be highly effective and cost-effective for one particular group of patients not receiving coverage and reimbursement because they are not cost-effective for everyone.

In contrast, when evaluating programmed cell death 1 receptor agents in the treatment of non–small cell lung cancer, ICER’s analyses relied on phase 2 and 3 trials that often did not have the power to establish subgroup effects reliably.14 Therefore, despite emerging practice-based evidence that testing the level of programmed cell death ligand 1 protein that a tumor expresses can significantly help determine which patients may benefit from treatment, there was no reliable evidence during the clinical trial stage of development to model treatment-effect heterogeneity and report subgroup analyses.

These cases demonstrate 2 key barriers to driving greater reflection of heterogeneity in policy choices: positioning and availability of sources. The first case shows that even when clear evidence of heterogeneity of effect is present in published evidence, it is not moved to the front of the conversation, perhaps because it was not directly studied in the regulatory trial contexts. The second example points to the limitations when there is a distinct lack of strong empirical evidence on heterogeneity at the time clinical trials are conducted, but such evidence does emerge in clinical practice.


 
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